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Application Programming Interface Design and Scalability Questions

Designing application programming interfaces that remain reliable, performant, and maintainable at high scale. Candidates should understand how interface decisions affect scalability, availability, latency, and operational complexity and be able to reason about trade offs between client complexity and server responsibility. Core areas include stateless interface design, pagination and cursor strategies for large result sets, filtering and search optimization, payload minimization, batching and streaming, and techniques to reduce server load while preserving client experience. Resilience and operational controls include rate limiting and quota management, throttling, backpressure and flow control, retry semantics and idempotency patterns, error format design and explicit identification of retryable errors, and strategies for graceful degradation under overload. Evolution and compatibility topics include backward compatible versioning strategies, deprecation policies, contract design and testing approaches to avoid breaking consumers. Infrastructure and deployment considerations include API gateway and edge patterns, interaction with load balancers and traffic distribution, caching and content delivery, routing fault tolerance, health checks and canary rollout strategies, and observability through metrics, distributed tracing, and logging to support capacity planning and incident response. Security considerations such as scalable authentication and authorization, credential and key management, and permission models are also important. Candidates should be prepared to discuss concrete patterns, trade offs, algorithms, and operational playbooks for designing and running high traffic application programming interfaces.

MediumTechnical
60 practiced
List techniques to minimize payload size for mobile API clients, including field projections, compression, binary encodings, delta sync, pagination, and server-driven content negotiation. For each technique describe server and client implications, CPU and bandwidth tradeoffs, and compatibility concerns when evolving schemas.
HardTechnical
115 practiced
Create a deprecation and migration policy for an API version used by thousands of clients across many industries. Include proactive detection of active clients, communication cadence, sunset timelines, SDK and migration tooling, automated enforcement thresholds, and rollback/contingency plans when migration is slow or causes regressions.
MediumTechnical
77 practiced
Implement a thread-safe token bucket rate limiter in Python that supports per-user rate limits and burst capacity. Provide an allow_request(user_id) method returning True/False. Explain how your design is efficient in memory, how you handle concurrency, and how you would adapt it to run across multiple API gateway instances in production.
MediumTechnical
77 practiced
Design an API error schema that explicitly marks whether an error is retryable by the client. Include fields for machine-readable error code, human message, retry-after metadata, suggested backoff strategy, and documentation link. Provide example payloads for a transient DB lock and an authentication failure, explaining how clients should react.
HardTechnical
61 practiced
Design a system to enforce global rate limits such as 1000 requests per minute per user across multiple regions with low per-request latency. Discuss algorithms (fixed window, sliding window, token bucket), data stores (local counters, Redis, CRDTs), trade-offs between strong and eventual consistency, and how to support burst behavior and reconciliation of counters after partitions.

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